PSPG Bootcamp 2020: Data Manipulation and Plotting in R
Vincent Chang and Jackie Ernest
2020-09-08
Objectives:
1) Learn the basics of dplyr
-Selecting, Filtering, and Arranging
-Making a series of function with pipes %>%
-Mutate new and existing columns
-Wide and long tables
2) Learn the basics of ggplot2
-Setting up your dataframe for easy plotting
-Plot lines, scatter plots, boxplots
-Compare different subpopulations
Installing and Loading packages
#Comment out if you've already installed them
#install.packages("dplyr")
#install.packages("tidyr")
#install.packages("ggplot2")
#Load libraries
library(dplyr)
library(tidyr)
library(ggplot2)Loading your dataset
#setwd("~/Documents/Courses/Bootcamp/bootcamp_2020/")
PKdata <- read.csv("simtab.csv")
#Let's take a glimpse at your dataset
head(PKdata)## ID PT_BIRTHDATE PT_USUBJID PT_ADDRESS TIME IPRED
## 1 1 09/20/1982 XDP5409 7385 San Francisco Drive 0 0.00000
## 2 1 09/20/1982 XDP5409 7385 San Francisco Drive 1 0.35767
## 3 1 09/20/1982 XDP5409 7385 San Francisco Drive 2 0.45474
## 4 1 09/20/1982 XDP5409 7385 San Francisco Drive 4 0.43277
## 5 1 09/20/1982 XDP5409 7385 San Francisco Drive 8 0.29634
## 6 1 09/20/1982 XDP5409 7385 San Francisco Drive 12 0.19763
## DV AMT HT BW SEX HIV IWRES CWRES PRED RES WRES DOSE
## 1 0.000000 300 69 78.298 1 0 NA 0 NA 0.0000000 0 300
## 2 2.840000 0 69 78.298 1 0 -10 0 0.36325 0.0058536 0 300
## 3 2.806300 0 69 78.298 1 0 -10 0 0.47078 0.0268820 0 300
## 4 1.497700 0 69 78.298 1 0 -10 0 0.46926 0.0861620 0 300
## 5 0.145240 0 69 78.298 1 0 -10 0 0.35684 -0.0715120 0 300
## 6 0.025496 0 69 78.298 1 0 -10 0 0.26497 -0.0797670 0 300#Alternatively, click on PKdata in your Global Environment toolbar on the right
#Let's open up the data dictionary too, it's always good practice to know your datasetUnderstanding your dataset
#Below are a few other ways to get a high-level glance at your data
glimpse(PKdata)## Observations: 6,300
## Variables: 18
## $ ID <int> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, …
## $ PT_BIRTHDATE <fct> 09/20/1982, 09/20/1982, 09/20/1982, 09/20/1982, 09/…
## $ PT_USUBJID <fct> XDP5409, XDP5409, XDP5409, XDP5409, XDP5409, XDP540…
## $ PT_ADDRESS <fct> 7385 San Francisco Drive, 7385 San Francisco Drive,…
## $ TIME <int> 0, 1, 2, 4, 8, 12, 24, 0, 1, 2, 4, 8, 12, 24, 0, 1,…
## $ IPRED <dbl> 0.000000, 0.357670, 0.454740, 0.432770, 0.296340, 0…
## $ DV <dbl> 0.0000000, 2.8400000, 2.8063000, 1.4977000, 0.14524…
## $ AMT <int> 300, 0, 0, 0, 0, 0, 0, 300, 0, 0, 0, 0, 0, 0, 300, …
## $ HT <int> 69, 69, 69, 69, 69, 69, 69, 59, 59, 59, 59, 59, 59,…
## $ BW <dbl> 78.298, 78.298, 78.298, 78.298, 78.298, 78.298, 78.…
## $ SEX <int> 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, …
## $ HIV <int> 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, …
## $ IWRES <int> NA, -10, -10, -10, -10, -10, -10, NA, -10, -10, -10…
## $ CWRES <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ PRED <dbl> NA, 0.363250, 0.470780, 0.469260, 0.356840, 0.26497…
## $ RES <dbl> 0.0000000, 0.0058536, 0.0268820, 0.0861620, -0.0715…
## $ WRES <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ DOSE <int> 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 3…summary(PKdata)## ID PT_BIRTHDATE PT_USUBJID
## Min. : 1.0 09/20/1982:6300 XDP1255: 14
## 1st Qu.:225.8 XDP1529: 14
## Median :450.5 XDP1868: 14
## Mean :450.5 XDP2059: 14
## 3rd Qu.:675.2 XDP2093: 14
## Max. :900.0 XDP2158: 14
## (Other):6216
## PT_ADDRESS TIME IPRED
## 5511 San Francisco Drive: 21 Min. : 0.000 Min. :0.0000
## 5647 San Francisco Drive: 21 1st Qu.: 1.000 1st Qu.:0.2331
## 1325 San Francisco Drive: 14 Median : 4.000 Median :0.4746
## 1499 San Francisco Drive: 14 Mean : 7.286 Mean :0.5746
## 1752 San Francisco Drive: 14 3rd Qu.:12.000 3rd Qu.:0.9266
## 2202 San Francisco Drive: 14 Max. :24.000 Max. :1.6115
## (Other) :6202
## DV AMT HT BW
## Min. : 0.00000 Min. : 0.00 Min. :46.00 Min. :36.21
## 1st Qu.: 0.03386 1st Qu.: 0.00 1st Qu.:60.00 1st Qu.:54.71
## Median : 0.59356 Median : 0.00 Median :64.00 Median :59.82
## Mean : 2.25156 Mean : 85.71 Mean :64.18 Mean :60.00
## 3rd Qu.: 3.54982 3rd Qu.: 0.00 3rd Qu.:68.00 3rd Qu.:65.43
## Max. :13.68900 Max. :900.00 Max. :84.00 Max. :83.69
##
## SEX HIV IWRES CWRES PRED
## Min. :0.0 Min. :0.0000 Min. :-10 Min. :0 Min. :0.0706
## 1st Qu.:0.0 1st Qu.:0.0000 1st Qu.:-10 1st Qu.:0 1st Qu.:0.3590
## Median :0.5 Median :0.0000 Median :-10 Median :0 Median :0.5854
## Mean :0.5 Mean :0.1956 Mean :-10 Mean :0 Mean :0.6706
## 3rd Qu.:1.0 3rd Qu.:0.0000 3rd Qu.:-10 3rd Qu.:0 3rd Qu.:0.9429
## Max. :1.0 Max. :1.0000 Max. :-10 Max. :0 Max. :1.4176
## NA's :900 NA's :900
## RES WRES DOSE
## Min. :-0.5040400 Min. :0 Min. :300
## 1st Qu.:-0.0515205 1st Qu.:0 1st Qu.:300
## Median : 0.0000000 Median :0 Median :600
## Mean :-0.0002373 Mean :0 Mean :600
## 3rd Qu.: 0.0461260 3rd Qu.:0 3rd Qu.:900
## Max. : 0.7366200 Max. :0 Max. :900
## #Don't know what a function does? Add a question mark to view its documentation
?table
#You can use table to compare counts of different variables
table(PKdata$SEX, PKdata$HIV)##
## 0 1
## 0 2541 609
## 1 2527 623#To add titles
table("SEX" = PKdata$SEX, "HIV" = PKdata$HIV)## HIV
## SEX 0 1
## 0 2541 609
## 1 2527 623#How many females were in each dose level?
table("DOSE" = PKdata$DOSE,"SEX" = PKdata$SEX)## SEX
## DOSE 0 1
## 300 1001 1099
## 600 1036 1064
## 900 1113 987Dplyr Cheatsheet https://raw.githubusercontent.com/rstudio/cheatsheets/master/data-transformation.pdf
Learn the basics of dplyr
Selecting
#You can select specific columns using select()
#Let's de-identify our dataset by selecting the columns we want
deidentified <- select(PKdata, ID, TIME, DV, DOSE, HT, BW, SEX, HIV)
head(deidentified)## ID TIME DV DOSE HT BW SEX HIV
## 1 1 0 0.000000 300 69 78.298 1 0
## 2 1 1 2.840000 300 69 78.298 1 0
## 3 1 2 2.806300 300 69 78.298 1 0
## 4 1 4 1.497700 300 69 78.298 1 0
## 5 1 8 0.145240 300 69 78.298 1 0
## 6 1 12 0.025496 300 69 78.298 1 0#You can also do this by "subtracting" the columns you don't want with -
deidentified <- select(PKdata, -PT_BIRTHDATE, -PT_ADDRESS, -PT_USUBJID)
head(deidentified)## ID TIME IPRED DV AMT HT BW SEX HIV IWRES CWRES PRED
## 1 1 0 0.00000 0.000000 300 69 78.298 1 0 NA 0 NA
## 2 1 1 0.35767 2.840000 0 69 78.298 1 0 -10 0 0.36325
## 3 1 2 0.45474 2.806300 0 69 78.298 1 0 -10 0 0.47078
## 4 1 4 0.43277 1.497700 0 69 78.298 1 0 -10 0 0.46926
## 5 1 8 0.29634 0.145240 0 69 78.298 1 0 -10 0 0.35684
## 6 1 12 0.19763 0.025496 0 69 78.298 1 0 -10 0 0.26497
## RES WRES DOSE
## 1 0.0000000 0 300
## 2 0.0058536 0 300
## 3 0.0268820 0 300
## 4 0.0861620 0 300
## 5 -0.0715120 0 300
## 6 -0.0797670 0 300#You can also search through your columns with helpers: contains(), starts_with(), ends_with()
What_did_this_do <- select(PKdata, ends_with("V"))
head(What_did_this_do)## DV HIV
## 1 0.000000 0
## 2 2.840000 0
## 3 2.806300 0
## 4 1.497700 0
## 5 0.145240 0
## 6 0.025496 0#Write your answer commented out below:
#removed all columns that end with V
#Can you use helpers to create the same deidentified dataset?
deidentified <- select(PKdata, -starts_with("PT"))
head(deidentified)## ID TIME IPRED DV AMT HT BW SEX HIV IWRES CWRES PRED
## 1 1 0 0.00000 0.000000 300 69 78.298 1 0 NA 0 NA
## 2 1 1 0.35767 2.840000 0 69 78.298 1 0 -10 0 0.36325
## 3 1 2 0.45474 2.806300 0 69 78.298 1 0 -10 0 0.47078
## 4 1 4 0.43277 1.497700 0 69 78.298 1 0 -10 0 0.46926
## 5 1 8 0.29634 0.145240 0 69 78.298 1 0 -10 0 0.35684
## 6 1 12 0.19763 0.025496 0 69 78.298 1 0 -10 0 0.26497
## RES WRES DOSE
## 1 0.0000000 0 300
## 2 0.0058536 0 300
## 3 0.0268820 0 300
## 4 0.0861620 0 300
## 5 -0.0715120 0 300
## 6 -0.0797670 0 300Filtering
#You can filter out specific rows using filter()
#For example, selecting all patients who received a specific dose
Dosed_600 <- filter(PKdata, DOSE == 600)
head(Dosed_600)## ID PT_BIRTHDATE PT_USUBJID PT_ADDRESS TIME IPRED
## 1 301 09/20/1982 XDP5607 1296 San Francisco Drive 0 0.00000
## 2 301 09/20/1982 XDP5607 1296 San Francisco Drive 1 0.73153
## 3 301 09/20/1982 XDP5607 1296 San Francisco Drive 2 0.95626
## 4 301 09/20/1982 XDP5607 1296 San Francisco Drive 4 0.97317
## 5 301 09/20/1982 XDP5607 1296 San Francisco Drive 8 0.77547
## 6 301 09/20/1982 XDP5607 1296 San Francisco Drive 12 0.60406
## DV AMT HT BW SEX HIV IWRES CWRES PRED RES WRES DOSE
## 1 0.00000 600 70 64.202 1 0 NA 0 NA 0.000000 0 600
## 2 6.34130 0 70 64.202 1 0 -10 0 0.72684 0.027128 0 600
## 3 7.36440 0 70 64.202 1 0 -10 0 0.94255 0.137470 0 600
## 4 4.15730 0 70 64.202 1 0 -10 0 0.94086 0.066440 0 600
## 5 0.98469 0 70 64.202 1 0 -10 0 0.71775 0.019561 0 600
## 6 0.28794 0 70 64.202 1 0 -10 0 0.53471 0.073796 0 600#You can use any logical function in R to designate your filter criteria
# > greater than, <= greater equal than
# < less than, <= lesser equal than
# ! not
# & and
# | or
#Can you filter for patients with body weight greater or equal than 45kg?
WT.GT.45 <- filter(PKdata, BW >= 45)
head(WT.GT.45)## ID PT_BIRTHDATE PT_USUBJID PT_ADDRESS TIME IPRED
## 1 1 09/20/1982 XDP5409 7385 San Francisco Drive 0 0.00000
## 2 1 09/20/1982 XDP5409 7385 San Francisco Drive 1 0.35767
## 3 1 09/20/1982 XDP5409 7385 San Francisco Drive 2 0.45474
## 4 1 09/20/1982 XDP5409 7385 San Francisco Drive 4 0.43277
## 5 1 09/20/1982 XDP5409 7385 San Francisco Drive 8 0.29634
## 6 1 09/20/1982 XDP5409 7385 San Francisco Drive 12 0.19763
## DV AMT HT BW SEX HIV IWRES CWRES PRED RES WRES DOSE
## 1 0.000000 300 69 78.298 1 0 NA 0 NA 0.0000000 0 300
## 2 2.840000 0 69 78.298 1 0 -10 0 0.36325 0.0058536 0 300
## 3 2.806300 0 69 78.298 1 0 -10 0 0.47078 0.0268820 0 300
## 4 1.497700 0 69 78.298 1 0 -10 0 0.46926 0.0861620 0 300
## 5 0.145240 0 69 78.298 1 0 -10 0 0.35684 -0.0715120 0 300
## 6 0.025496 0 69 78.298 1 0 -10 0 0.26497 -0.0797670 0 300#You can also use & or | to create more complex logical functions
#Let's filter for patients that received a dose greater than or equal to 600 and HIV+
Dosed600up_HIV <- filter(PKdata, DOSE >= 600 & HIV == 1)
head(Dosed600up_HIV)## ID PT_BIRTHDATE PT_USUBJID PT_ADDRESS TIME IPRED
## 1 315 09/20/1982 XDP4217 7593 San Francisco Drive 0 0.00000
## 2 315 09/20/1982 XDP4217 7593 San Francisco Drive 1 0.73035
## 3 315 09/20/1982 XDP4217 7593 San Francisco Drive 2 0.95281
## 4 315 09/20/1982 XDP4217 7593 San Francisco Drive 4 0.96497
## 5 315 09/20/1982 XDP4217 7593 San Francisco Drive 8 0.76058
## 6 315 09/20/1982 XDP4217 7593 San Francisco Drive 12 0.58588
## DV AMT HT BW SEX HIV IWRES CWRES PRED RES WRES DOSE
## 1 0.00000 600 64 65.279 1 1 NA 0 NA 0.00000000 0 600
## 2 5.87530 0 64 65.279 1 1 -10 0 0.71913 0.01188800 0 600
## 3 5.68390 0 64 65.279 1 1 -10 0 0.92031 -0.00044695 0 600
## 4 3.47330 0 64 65.279 1 1 -10 0 0.88979 0.16336000 0 600
## 5 0.67189 0 64 65.279 1 1 -10 0 0.63150 0.20880000 0 600
## 6 0.12514 0 64 65.279 1 1 -10 0 0.43691 0.10928000 0 600#Challenge: Filter for patients who received the lowest or highest dose, and is female with HIV or male
#Remember there are always many ways to code something :)
Challenge <- filter(PKdata, DOSE != 600 & (HIV == 1 | SEX == 1))
head(Challenge)## ID PT_BIRTHDATE PT_USUBJID PT_ADDRESS TIME IPRED
## 1 1 09/20/1982 XDP5409 7385 San Francisco Drive 0 0.00000
## 2 1 09/20/1982 XDP5409 7385 San Francisco Drive 1 0.35767
## 3 1 09/20/1982 XDP5409 7385 San Francisco Drive 2 0.45474
## 4 1 09/20/1982 XDP5409 7385 San Francisco Drive 4 0.43277
## 5 1 09/20/1982 XDP5409 7385 San Francisco Drive 8 0.29634
## 6 1 09/20/1982 XDP5409 7385 San Francisco Drive 12 0.19763
## DV AMT HT BW SEX HIV IWRES CWRES PRED RES WRES DOSE
## 1 0.000000 300 69 78.298 1 0 NA 0 NA 0.0000000 0 300
## 2 2.840000 0 69 78.298 1 0 -10 0 0.36325 0.0058536 0 300
## 3 2.806300 0 69 78.298 1 0 -10 0 0.47078 0.0268820 0 300
## 4 1.497700 0 69 78.298 1 0 -10 0 0.46926 0.0861620 0 300
## 5 0.145240 0 69 78.298 1 0 -10 0 0.35684 -0.0715120 0 300
## 6 0.025496 0 69 78.298 1 0 -10 0 0.26497 -0.0797670 0 300Arranging
#arrange() will reorder rows or columns according to your specifications (default from low to high)
#For example in PK data we will often organize our rows by patient ID, then by time.
PKdata <- arrange(PKdata, ID, TIME)Putting it all together with Pipes %>%
#Pipes are used to connect the output of one function to the input of another
#Let's deidentify our dataset, filter for 600mg dose, and arrange by id and time all together
Dose_600 <- PKdata %>%
select( -starts_with("PT")) %>%
filter(DOSE == 600) %>%
arrange(ID, TIME)
#Try this one for yourself!
#Let's try filtering for HIV+ or body weight less than 45kg, then...
#we want to see the trough levels of each patient (defined as the 24hr timepoint)
Trough <- PKdata %>%
select( -starts_with("PT")) %>%
filter(HIV == 1 | BW < 45) %>%
filter(TIME == 24)
View(Trough)Mutate
#mutate() can create new columns or alter existing ones
#For example, BMI is often calculated from HT and WT
#BMI = weight(kg)/height(m)^2
PKdata <- PKdata %>%
mutate(BMI = BW/(HT/39.37)^2) #are the units correct? check the data dictionary!
#Now you try making a new column and converting DV from mg/L into micromoles!
#The drug used here is remdesivir
PKdata<- PKdata %>%
mutate(Conc_uM = DV/1000/602.6*1000000) #mg/L *1g/1000mg * 1mol/602.6g * 1000000uM/1MLearn the basics of ggplot2
Our first Plot!!
ggplot2 cheatsheet: https://raw.githubusercontent.com/rstudio/cheatsheets/master/data-visualization-2.1.pdf
ggplot(PKdata) +
geom_point(aes(x=TIME, y=DV)) ##What are we trying to plot with a PK profile?
#Do you like lines better?
ggplot(PKdata) +
geom_line(aes(x=TIME, y=DV,group=factor(ID)),alpha = 0.5, linetype=2,size=0.1)
#Let's put them on the same graph with a +
ggplot(PKdata) +
geom_line(aes(x=TIME, y=DV,group=factor(ID)),alpha = 0.5, linetype=2,size=0.1) +
geom_point(aes(x=TIME, y=DV))
Graphing Subpopulations
ggplot(PKdata) +
geom_point(aes(x=TIME, y=DV, color = as.factor(DOSE))) +
geom_line(aes(x=TIME, y=DV, group=factor(ID), color = as.factor(DOSE)),alpha = 0.5, linetype=2,size=0.1)
Another way to stratify
#It's still a little too cluttered
ggplot(PKdata) +
geom_point(aes(x=TIME, y=DV, color = as.factor(DOSE))) +
geom_line(aes(x=TIME, y=DV, group=factor(ID), color = as.factor(DOSE)),alpha = 0.5, linetype=2,size=0.1) +
facet_wrap(.~DOSE)
Let’s try some on your own
#Copy and paste from above, and let's plot stratified by HIV status
ggplot(PKdata) +
geom_point(aes(x=TIME, y=DV, color = as.factor(HIV))) +
geom_line(aes(x=TIME, y=DV, group=factor(ID), color = as.factor(HIV)),alpha = 0.5, linetype=2,size=0.1) +
facet_wrap(.~DOSE)
Two stratifications
#Now let's try adding 2 stratifications: color by HIV and facet_wrap by SEX~DOSE. Then try the other way around!
ggplot(PKdata) +
geom_point(aes(x=TIME, y=DV, color = as.factor(HIV))) +
geom_line(aes(x=TIME, y=DV, group=factor(ID), color = as.factor(HIV)),alpha = 0.5, linetype=2,size=0.1) +
facet_wrap(SEX~DOSE)
ggplot(PKdata) +
geom_point(aes(x=TIME, y=DV, color = as.factor(SEX))) +
geom_line(aes(x=TIME, y=DV, group=factor(ID), color = as.factor(SEX)),alpha = 0.5, linetype=2,size=0.1) +
facet_wrap(HIV~DOSE)
#Challenge: Plot stratified by body weight, one color for above 45kg and another color for below. Make sure to have a legend :)
#Hint: you will need to manipulate your dataframe
PKdata <- PKdata %>%
mutate(WT_FLAG = ifelse(BW > 45, 1, 0))
ggplot(PKdata) +
geom_point(aes(x=TIME, y=DV, color = as.factor(WT_FLAG))) +
geom_line(aes(x=TIME, y=DV, group=factor(ID), color = as.factor(WT_FLAG)),alpha = 0.5, linetype=2,size=0.1) +
facet_wrap(.~DOSE)
Summarise
Dplyr can be used for simple summary statistics
#You can use summarise to calculate means, ranges, standard deviations, quartiles, etc.
Summary_stats <- PKdata %>%
summarise(c_mean = mean(DV),
c_stdev = sd(DV),
BW_mean = mean(BW),
BW_upper = quantile(BW, probs = 0.975),
BW_lower = quantile(BW, probs = 0.225))
Summary_stats## c_mean c_stdev BW_mean BW_upper BW_lower
## 1 2.251562 2.915612 59.99685 76.187 53.955Combined with group_by(), summarise is a force to be reckoned with..
#Let's calculate subpopulation summary statistics
#For example, let's calculate body weight summary statistics by SEX
BW_by_SEX <- PKdata %>%
group_by(SEX) %>%
summarise(BW_mean = mean(BW),
BW_upper = quantile(BW, probs = 0.975),
BW_lower = quantile(BW, probs = 0.225))
#Let's plot it!
ggplot(BW_by_SEX) +
geom_point(aes(x=SEX, y=BW_mean)) +
geom_errorbar((aes(x=SEX, ymin=BW_lower, ymax=BW_upper)))
#An easier way to do it in just ggplot, there are always multiple ways to do the same thing
ggplot(PKdata) +
geom_boxplot(aes(x=as.factor(SEX), y=BW))
Let’s calculate the median PK profile
#Let's start with calculating median and percentile profiles
Med <- PKdata %>%
group_by(TIME) %>%
summarise(median = median(DV),
upper = quantile(DV, probs=0.975), #Upper 95th percentile
lower = quantile(DV, probs=0.225)) #Lower 95th percentile
#Let's plot it
ggplot(Med) +
geom_line(aes(x=TIME, y=median)) +
geom_ribbon(aes(x=TIME, ymin=lower, ymax=upper), alpha=0.3)
Let’s compare median PK profiles of subpopulations
#Let's do the same thing but subset by DOSE
DOSE_Med <- PKdata %>%
group_by(DOSE,TIME) %>%
summarise(median = median(DV),
upper = quantile(DV, probs=0.975), #Upper 95th percentile
lower = quantile(DV, probs=0.225)) #Lower 95th percentile
#Let's plot it
ggplot(DOSE_Med) +
geom_line(aes(x=TIME, y=median, color = as.factor(DOSE))) +
geom_ribbon(aes(x=TIME, ymin=lower, ymax=upper, fill = as.factor(DOSE)), alpha=0.3)
Let’s try another one…
#Median PK profiles, subset by HIV
HIV_Med <- PKdata %>%
group_by(HIV,TIME) %>%
summarise(median = median(DV),
upper = quantile(DV, probs=0.975), #Upper 95th percentile
lower = quantile(DV, probs=0.225)) #Lower 95th percentile
#Let's plot it
ggplot(HIV_Med) +
geom_line(aes(x=TIME, y=median, color = as.factor(HIV))) +
geom_ribbon(aes(x=TIME, ymin=lower, ymax=upper, fill = as.factor(HIV)), alpha=0.3)
This doesn’t work super well…the different dosing levels make the stratification cluttered
#We need to subset by DOSE and HIV
#Prepare the dataset yourself
HIV_Med <- PKdata %>%
group_by(DOSE, HIV,TIME) %>%
summarise(median = median(DV),
upper = quantile(DV, probs=0.975), #Upper 95th percentile
lower = quantile(DV, probs=0.225)) #Lower 95th percentile
#Let's plot it
ggplot(HIV_Med) +
geom_line(aes(x=TIME, y=median, color = as.factor(HIV))) +
geom_ribbon(aes(x=TIME, ymin=lower, ymax=upper, fill = as.factor(HIV)), alpha=0.3) +
facet_wrap(.~DOSE) +
scale_y_log10()## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis